@incollection{Quinlan01Relational,
        title = {Relational learning and boosting},
        address = {New York, NY, USA},
        author = {J. Ross Quinlan},
        booktitle = {Relational Data Mining},
        chapter = 12,
        editor = {Saso Dzeroski and Nada Lavrac},
        pages = {292--304},
        publisher = {Springer-Verlag New York, Inc.},
        year = 2001,
        url = {http://portal.acm.org/citation.cfm?id=567237#},
        timestamp = {2007.12.19},
	isbn = {3-540-42289-7},
	book = {Relational Data Mining},
	owner = {martin},
	description = {Relational learning and boosting},
	abstract = {Boosting, a methodology for constructing and combining multiple classifiers,
	has been found to lead to substantial improvements in predictive
	accuracy. Although boosting was formulated in a propositional learning
	context, the same ideas can be applied to first-order learning (also
	known as inductive logic programming). Boosting is used here with
	a system that learns relational definitions of functions. Results
	show that the occasional negative impact of boosting all resemble
	the corresponding observations for propositional learning.},
	biburl = {http://www.bibsonomy.org/bibtex/2cc01ba6febdfff75007432ecca0ad4da/mh},
	keywords = {inductive_logic_programming inductive_programming ILP FFOIL FOIL boosting}
}